Image texture removal is a kind of image processing technique that allows representing an overall shape of an object more clearly by removing a texture while preserving structural edges of an original image. Various studies on such the image texture removal techniques have been carried out, and the most common approach is to blend pixels such that they do not cross edge boundaries in consideration of the structural edges when smoothing the image.
Various methods have been proposed so far in relation to smoothing techniques that can preserve edges. One of the most widely used methods is a method using a bilateral filtering. The bilateral filtering is a filtering technique that does not blend areas with large color differences, and has been widely used for various applications due to its simplicity, effectiveness, and scalability.
In addition to the bilateral filtering, various methods for the image texture removal for preserving structural edges have been developed, including methods using weighted least squares, edge-avoiding wavelets, local histogram filtering, Laplacian pyramids, domain transformation, optimization for minimizing L0 gradients, and the like. Such the image texture removal methods have been proposed for the purpose of preserving the structural edges of an original image and smoothing fine-details of the original image, but since they do not process texture explicitly, there is a limit to be used for explicit texture removal.
As another edge-preserving smoothing method, there is a method of using local extrema to separate the fine-details of the image from a base layer of the image. Alternatively, regular or nearly regular textures can be found using spatial relationships, frequency, and symmetry of texture features. Also, total variation (TV) regularization is effective for removing irregularly-shaped textures, and is suitable for filtering to preserve large-scale edges. The TV regularization has been further extended to improve quality, robustness, and efficiency. Specifically, relative TV is a spatially varying TV measure of the image to improve quality when separating textures and image structure.
Recently, a patch-based texture removal method in which similarity of image patches is measured based on region covariance descriptors has been proposed. As compared to typical pixel-based image decomposition methods, the patch-based method uses a covariance matrix between each patch and neighboring patches so that more accurate detection of texture features becomes possible and the performance of separating textures from the structural edges can be enhanced. However, the patch-based method has a problem that the patches at the edge boundaries are overlapped to have similar statistical characteristics and thus the structural edges are smudged.
The proposed methods have the limitation that they are difficult to implement, accelerate, scale, and adapt their algorithms due to the computational complexity of them despite effective smoothing. Also, since the conventional image texture removal techniques mainly focus on the smoothing technique that preserves the edges, there is a limitation that the textures are not explicitly processed and the textures cannot be clearly removed. Also, in the case of the patch-based texture removal method that explicitly processes textures, there is a limit in that acceleration, expansion, and adaptation of its algorithm are also difficult due to computational complexity needed for image optimization and regularization calculation.